{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cd /home/daniel/Code/2019/pypcd_daniel/pypcd && /usr/bin/python3 /home/daniel/Code/2019/pypcd_daniel/pypcd/setup.py -q install --user\n"
     ]
    }
   ],
   "source": [
    "# Install pypcd from this repository\n",
    "import notebook_helper\n",
    "!{notebook_helper.get_install_cmd(quiet=True)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1.1\n"
     ]
    }
   ],
   "source": [
    "import pypcd\n",
    "print(pypcd.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# necessary for python3\n",
    "from pypcd import pypcd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Intentionally pasting the example point cloud into this cell\n",
    "#   so the reader can inspect the ascii file format\n",
    "#\n",
    "#   Source: http://pointclouds.org/documentation/tutorials/pcd_file_format.php\n",
    "\n",
    "pcd_string = \"\"\"# .PCD v.7 - Point Cloud Data file format\n",
    "VERSION .7\n",
    "FIELDS x y z rgb\n",
    "SIZE 4 4 4 4\n",
    "TYPE F F F F\n",
    "COUNT 1 1 1 1\n",
    "WIDTH 213\n",
    "HEIGHT 1\n",
    "VIEWPOINT 0 0 0 1 0 0 0\n",
    "POINTS 213\n",
    "DATA ascii\n",
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    "-0.18369 -0.23729 0 4.808e+06\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load cloud.pcd for visualization\n",
    "cloud = pypcd.PointCloud.from_buffer(pcd_string)\n",
    "\n",
    "# set the size of pyplot charts\n",
    "plt.rcParams['figure.figsize'] = (14, 6)\n",
    "\n",
    "# plot the points of the columns x and y\n",
    "plt.scatter(cloud.pc_data['x'], -cloud.pc_data['y'])\n",
    "\n",
    "# scale the axis equally \n",
    "plt.axis('scaled');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'count': [1, 1, 1, 1],\n",
       " 'data': 'ascii',\n",
       " 'fields': ['x', 'y', 'z', 'rgb'],\n",
       " 'height': 1,\n",
       " 'points': 213,\n",
       " 'size': [4, 4, 4, 4],\n",
       " 'type': ['F', 'F', 'F', 'F'],\n",
       " 'version': '.7',\n",
       " 'viewpoint': [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0],\n",
       " 'width': 213}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cloud.get_metadata()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
